The life limiting event of modern rockets is typically structural failure of the solid propellant. A failure may be a crack in the propellant or an interface bond separation that causes an unacceptable deviation in thrust or over pressurization leading to case failure. Conventionally, current service life predictions for rocket motors treat large populations of rocket motors as equivalent for the risk of failure and retire the group when that risk exceeds a predetermined threshold. This process tends to be conservative and the desired reliability threshold set by the customer is typically very high. The result is numerous rocket motors are removed from service, even though they may have many years of service life left.
U.S. Pat. No. 6,301,970 to Biggs et al. entitled “CUMULATIVE DAMAGE MODEL FOR STRUCTURAL ANALYSIS OF FILED POLYMERIC MATERIALS,” the contents of which are herein incorporated by reference, discloses a stochastic cumulative damage model, A Global Engineering Model of Damage (AGEMOD), to forecast the probability of solid propellant structural failure. This process uses a simplified generic service history to create a temperature profile for the group of motors being modeled. A linear visco-elastic material model (LVEM) is employed to relate the temperatures the motors are subjected to, with the stresses induced in the propellant from thermal contraction. AGEMOD utilizes a Linear Cumulative Damage (LCD) calculation to forecast the reliability. This approach is a stochastic process because AGEMOD uses a Monte Carlo type simulation to model variation about the temperature and mechanical properties.
Additionally, significant advances have been made with respect to small stress sensors. These small stress sensors are miniature in size, produced in high volume, and utilize low power to provide data collection. There are a number of ongoing efforts to embed stress sensors in solid rocket motors, enabling collection of data specific to each motor and reduction of errors in the damage calculation. Utilizing embedded sensors enables the load history of individual units to be collected and analyzed, as part of an individual service life determination. This approach enables the removal of just those units whose forecast reliability has dropped to unacceptable levels. The majority of the population can remain in use until those units experience an event or degradation of properties that cause their reliability to drop. This approach is a far more efficient approach to the management of the fielded inventory and provides significant cost savings.
However, the stress sensors provide data collection over many years resulting in hundreds of thousands or millions of data points. In order for this data to be useful, the volume of data must be reduced, while the significant damage events are retained.